import torch
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
import time

__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
           'wide_resnet50_2', 'wide_resnet101_2']


model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
}


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
    # This variant is also known as ResNet V1.5 and improves accuracy according to
    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.

    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self,block, layers, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None,partition_layer='input',end_layer='prediction',build = True):
        super(ResNet, self).__init__()
        #
        ##print("======resnet local======", partition_layer, end_layer,layers,build)
        self.partition_layer = partition_layer
        self.end_layer = end_layer
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layers_info = {"layer1":{"blocks":3,"planes":64,"stride":1,"dilate":False,"inplanes":[64,256]},
                            "layer2":{"blocks":4,"planes":128,"stride":2,"dilate":replace_stride_with_dilation[0],"inplanes":[256,512]},
                            "layer3":{"blocks":23,"planes":256,"stride":2,"dilate":replace_stride_with_dilation[1],"inplanes":[512,1024]},
                            "layer4":{"blocks":3,"planes":512,"stride":2,"dilate":replace_stride_with_dilation[2],"inplanes":[1024,2048]}}
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)
        if build and ("layer" in partition_layer or "layer" in end_layer):
            self._build_model(partition_layer,end_layer,block)



    def _build_model(self,partition_layer,end_layer,block):
        """
        get the index of the layer and the index of the sublayer
        the name for each layer is layer_x_y, where x is the layer index and the y is the sublayer index
        we need to change the structure of the partition layer and the end layer,
        and the layers between the two points stay the same
        """
        #print("+++++++++++build model++++++++++++===")
        start_layer_index,end_layer_index = None,None
        if partition_layer not in ['input','conv1']:
            start_layer_info = partition_layer.split('_')
            start_layer_index,start_sublayer_index = int(start_layer_info[1]),int(start_layer_info[2])
        if end_layer != 'prediction':
            end_layer_info = end_layer.split('_')
            end_layer_index,end_sublayer_index = int(end_layer_info[1]),int(end_layer_info[2])
        else:
            end_layer_index,end_sublayer_index = -1,-1
        #print("====wjwjwj=====", end_layer_index,end_sublayer_index)
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        # 1. change the structure of the partition_layer
        change_end_layer = False
        for i in range(1,5):
            layer_name = "layer"+str(i)
            if start_layer_index is not None and start_layer_index == i:
                stride = self.layers_info[layer_name]["stride"]
                planes = self.layers_info[layer_name]["planes"]
                blocks = self.layers_info[layer_name]["blocks"]
                inplanes = self.layers_info[layer_name]["inplanes"]
                if self.layers_info[layer_name]["dilate"]:
                    self.dilation *= self.layers_info[layer_name]["stride"]
                    stride = 1
                if stride != 1 or inplanes[0] != inplanes[1]:
                    downsample = nn.Sequential(
                        conv1x1(inplanes[0], inplanes[1], stride),
                        norm_layer(planes * block.expansion),
                    )
                # change layer 1
                if end_layer_index == i:
                    max_layer = end_sublayer_index
                    change_end_layer = True
                else:
                    max_layer = blocks-1
                layers = []
                for _ in range(start_sublayer_index+1, max_layer+1):
                    layers.append(block(inplanes[1], planes, groups=self.groups,
                                        base_width=self.base_width, dilation=self.dilation,
                                        norm_layer=norm_layer))

                if start_layer_index == 1:
                    self.layer1 = nn.Sequential(*layers)
                elif start_layer_index == 2:
                    self.layer2 = nn.Sequential(*layers)
                elif start_layer_index == 3:
                    self.layer3 = nn.Sequential(*layers)
                    #print(self.layer3)
                elif start_layer_index == 4:
                    self.layer4 = nn.Sequential(*layers)

            if not change_end_layer:
                if end_layer_index is not None and end_layer_index == i:
                    #print("^^^^^^^^^^", type(end_layer_index), end_layer_index is not None, end_layer_index == i)
                    stride = self.layers_info[layer_name]["stride"]
                    planes = self.layers_info[layer_name]["planes"]
                    blocks = self.layers_info[layer_name]["blocks"]
                    inplanes = self.layers_info[layer_name]["inplanes"]
                    if self.layers_info[layer_name]["dilate"]:
                        self.dilation *= self.layers_info[layer_name]["stride"]
                        stride = 1
                    if stride != 1 or inplanes[0] != inplanes[1]:
                        downsample = nn.Sequential(
                            conv1x1(inplanes[0], inplanes[1], stride),
                            norm_layer(planes * block.expansion),
                        )
                    layers = []
                    layers.append(block(inplanes[0], planes, stride, downsample, self.groups,
                                        self.base_width, previous_dilation, norm_layer))
                    if end_sublayer_index > 0:
                        for _ in range(1, end_sublayer_index + 1):
                            layers.append(block(inplanes[1], planes, groups=self.groups,
                                                base_width=self.base_width, dilation=self.dilation,
                                                norm_layer=norm_layer))
                    if end_layer_index == 1:
                        self.layer1 = nn.Sequential(*layers)
                        #print(self.layer1)
                    elif end_layer_index == 2:
                        self.layer2 = nn.Sequential(*layers)
                    elif end_layer_index == 3:
                        self.layer3 = nn.Sequential(*layers)
                    elif end_layer_index == 4:
                        self.layer4 = nn.Sequential(*layers)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False,layer_name=None):

        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
            #print("====come here=====")
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )
            #print("====come here 2=====")

        layers = []
        #sublayer_index = layer_name[layer_name.split("b")+1:] # get the index of the sublayer
        #print("first=======",planes,self.inplanes)
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        #print("later ========",self.inplanes)
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))
        #print()
        return nn.Sequential(*layers)

    def _forward_impl(self, x):
        # See note [TorchScript super()]
        #[3, 4, 23, 3]
        # "resnet_101": ['input', 'conv1', 'layer_1_0', 'layer_2_0',
        # 'layer_3_0', 'layer_3_1', 'layer_3_2', 'layer_3_3',
        # 'layer_3_4', 'layer_3_5','layer_3_6', 'prediction'],
        flag = False
        if flag or self.partition_layer == 'input':
            flag = True

            if self.end_layer == 'input':
                return x
            elif self.end_layer == 'conv1':
                x = self.conv1(x)
                x = self.bn1(x)
                x = self.relu(x)
                x = self.maxpool(x)
                #print("=-----conv1-------")
                return x
            else:
                #print("=-----image-------",x)
                x = self.conv1(x)
                x = self.bn1(x)
                x = self.relu(x)
                x = self.maxpool(x)
                #print("=-----conv1-------")

        '''
        'input','conv1','layer_1_0', 'layer_1_1', 'layer_1_2', 'layer_2_0', 'layer_2_1', 'layer_2_2',
        'layer_2_3', 'layer_3_0', 'layer_3_1', 'layer_3_2', 'layer_3_3', 'layer_3_4', 'layer_3_5',
        'layer_3_6', 'prediction'
        '''
        #print("==================",flag)
        if flag or self.partition_layer in ['conv1','layer_1_0', 'layer_1_1']:
            flag = True
            x = self.layer1(x)
            if 'layer_1' in self.end_layer:
                #print("=======layer_1========")
                return x

        if flag or self.partition_layer in ['layer_1_2', 'layer_2_0', 'layer_2_1', 'layer_2_2']:
            flag = True
            x = self.layer2(x)
            if 'layer_2' in self.end_layer:
                #print("=======layer_2========",self.partition_layer)
                return x
            #print("=======layer_2========", self.partition_layer)
        if flag or self.partition_layer == 'layer_2_3' or 'layer_3' in self.partition_layer :
            flag = True
            x = self.layer3(x)
            if 'layer_3' in self.end_layer:
                #print("=======layer_3========", self.partition_layer)
                return x
            #print("=======layer_3========", self.partition_layer)
        if flag:
                x = self.layer4(x)
                x = self.avgpool(x)
                x = torch.flatten(x, 1)
                x = self.fc(x)
                if self.end_layer == 'prediction':
                    #print("=======prediction========", self.partition_layer)
                    return x
                #print("=======prediction========", self.partition_layer)
    def forward(self, x):
        return self._forward_impl(x)


def _resnet(arch, block, layers, pretrained, progress,partition_layer,end_layer,build, **kwargs):
    model = ResNet(block, layers,partition_layer=partition_layer,end_layer=end_layer,build=build, **kwargs)
    if pretrained:
        #pint("========================",model_urls[arch])
        state_dict = load_state_dict_from_url(model_urls[arch],progress=progress)
        #print(type(state_dict))
        model.load_state_dict(state_dict,strict=False)
    return model

def resnet50(pretrained=True, progress=False, partition_layer='input',end_layer='prediction', **kwargs):
    r"""ResNet-50 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
                   **kwargs)

def resnet101(partition_layer='input',end_layer='prediction', build = True, pretrained=False, progress=True, **kwargs):
    r"""ResNet-101 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,partition_layer=partition_layer,end_layer=end_layer,build = build,
                   **kwargs)



